Performance-based haulage management system

ABSTRACT

A method for managing haul routes in work environments comprises collecting performance data associated with a machine operating in a work environment. The method also includes determining a drive axle torque of the machine and estimating a total effective grade associated with the machine based on the drive axle torque. The estimated total effective grade is compared with a threshold level and, if the estimated total effective grade exceeds the threshold level, a design performance of the machine may be simulated based on the calculated total effective grade. Design performance data is compared with the collected performance data, and a payload limit of the machine is adjusted if the design performance is inconsistent with collected performance data.

TECHNICAL FIELD

The present disclosure relates generally to fleet management inenvironments that employ heavy machinery and, more particularly, tosystems and methods for monitoring the performance of machines operatingin a machine environment and managing haulage requirements of eachmachine.

BACKGROUND

In many work environments, particularly those that employ a fleet ofmachines that cooperate to perform a common task, productivity andefficiency of one machine may have a significant impact on theproductivity and efficiency of other machines. For example, many largefleets include a combination of old and new machines. The old machinesmay be slower, more susceptible to fatigue, and less productive than newmachines. In certain situations, the limited performance capabilities ofthe older machines may limit the productivity of the newer machines and,ultimately, the productivity of the work environment.

In mining environments, for example, dump trucks or haulers may be usedto transport material from an excavation site to a production ordelivery site via a haul route. Because mine operators are typicallycompensated based on the weight of material that is excavated from themine and delivered for production, the profitability of the miningenvironment may ultimately depend on the speed and efficiency with whichmaterial is transported from the excavation site to the delivery site.In order to maximize the profitability of the mining environment, it maybe advantageous to monitor and regulate the operation of each machineusing the haul route, so that slower, less capable machines do notnegatively impact faster, more productive machines.

One system for monitoring and regulating the operation of one or morevehicles operating in a common environment is described in U.S. Pat. No.6,246,932 (“the '932 patent”) to Kageyama et al. The '932 patentdescribes a vehicle monitoring system for directing or controllingmovements of a plurality of vehicles operating in a worksite to minimizethe interference between, and avoid the collision of, one or more of thevehicles. The vehicle monitoring system of the '932 patent includesposition measuring equipment for measuring a position of one or morevehicles within the worksite. The vehicle monitoring system may beconfigured to calculate the travel distance, bearing, and speed of thevehicles. Based on the monitored position and calculated traveldistance, bearing, and speed of the vehicles, the vehicle monitoringsystem may provide speed and directional commands to vehicle operators.These commands may limit the potential for vehicle collisions and reducecongestion in the worksite. In some cases, if the command signals arenot obeyed, the vehicle monitoring system may override the operatorcontrols and cause the machine to take appropriate measures (e.g., pullover, decelerate, etc.) to avoid a potential collision.

Although conventional systems may increase worksite efficiency byregulating speed and bearing of traveling vehicles to reduce congestion,they may be insufficient in certain situations. For example, manyconventional systems, such as the system described in the '932 patent,merely provide commands to vehicle operators to adjust operating speedand/or bearing after the vehicle is underway, so as to avoidinterference with other vehicles during vehicle travel. These systems,however, may not identify performance flaws of underperforming vehiclesand take measures to prevent these flaws from affecting operations ofother vehicles.

Furthermore, conventional vehicle monitoring systems may not be equippedto regulate the loading characteristics of the vehicles based onperformance characteristics of the vehicles. As a result, workenvironments that routinely load vehicles to their maximum capacity mayunnecessarily overburden slow or underperforming vehicles, which may, inturn, limit or reduce the maximum speed that these vehicles can travel.In doing so, these overburdened vehicles may unnecessarily limit thespeed of following vehicles, thereby potentially slowing operationswithin the work environment and reducing worksite productivity.Conventional systems that merely adjust the speed of one or morevehicles so as to avoid interference between the vehicles may notadequately address performance changes of each vehicle based on thepayload levels corresponding thereto.

The presently disclosed performance-based haulage management system isdirected toward overcoming one or more of the problems set forth above.

SUMMARY OF THE INVENTION

In accordance with one aspect, the present disclosure is directed towarda method for managing haul routes in work environments. The method mayinclude collecting performance data associated with a machine during ahaul route run in a work environment and monitoring a route completiontime associated with the machine. The route completion time may becompared with a threshold completion time and, if the route completiontime exceeds the threshold completion time, a first payload level forthe machine may be selected. Using a model derived from the performancedata of the machine, operation of the machine may be simulated based onthe first payload level to generate a simulated route completion time ofthe machine. If the simulated route completion time does not exceed thethreshold completion time, a payload limit of the machine may beestablished as the first payload level.

According to another aspect, the present disclosure is directed toward amethod for managing haul routes in work environments. The method mayinclude collecting performance data associated with a machine operatingin a work environment. The method may also include determining a driveaxle torque of the machine and calculating a total effective gradeassociated with the machine based on the drive axle torque. Theestimated total effective grade may be compared with a threshold leveland, if the estimated total effective grade exceeds the threshold level,a design performance of the machine may be simulated based on thecalculated total effective grade. The design performance data may becompared with the collected performance data, whereby a payload limit ofthe machine may be adjusted if the design performance is inconsistentwith collected performance data.

In accordance with yet another aspect, the present disclosure isdirected toward a haul route management system. The haul routemanagement system may include a condition monitoring system in datacommunication with a machine operating in a work environment andconfigured to collect performance data associated with the machine. Thesystem may also include a torque estimator communicatively coupled tothe condition monitoring system and configured to determine a drive axletorque of the machine and estimate a total effective grade associatedwith the machine based on the drive axle torque. The system may furtherinclude a performance simulator communicatively coupled to the torqueestimator and the condition monitoring system. The performance simulatormay be configured to compare the estimated total effective grade with athreshold level. If the estimated total effective grade exceeds thethreshold level, a design performance of the machine may be simulatedbased on the estimated total effective grade. The design performancedata may be compared with the collected performance data, and a payloadanalysis of the machine may be performed if the collected performancedata is inconsistent with the design performance data. The method mayalso include establishing a payload limit of the machine based on thepayload analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary work environment consistent with thedisclosed embodiments;

FIG. 2 provides a schematic diagram illustrating certain componentsassociated with the work environment of FIG. 1;

FIG. 3 provides a flowchart depicting one exemplary method for managinghaul routes in work environments, consistent with certain disclosedembodiments; and

FIG. 4 provides a flowchart depicting another exemplary method formanaging haul routes in work environments, consistent with certaindisclosed embodiments.

DETAILED DESCRIPTION

FIG. 1 illustrates an exemplary work environment 100 consistent with thedisclosed embodiments. Work environment 100 may include systems anddevices that cooperate to perform a commercial or industrial task, suchas mining, construction, energy exploration and/or generation,manufacturing, transportation, agriculture, or any task associated withother types of industries. According to the exemplary embodimentillustrated in FIG. 1, work environment 100 may include a miningenvironment that comprises one or more machines 120 a, 120 b coupled toa haul route management system 135 via a communication network 130. Workenvironment 100 may be configured to monitor, collect, and filterinformation associated with the status, health, and performance of oneor more machines 120 a, 120 b, and distribute the information to one ormore back-end systems or entities, such as haul route management system135 and/or subscribers 170. It is contemplated that additional and/ordifferent components than those listed above may be included in workenvironment 100.

As illustrated in FIG. 1, machines 120 a, 120 b may include one or moreexcavators 120 a and one or more transport machines 120 b. Excavators120 a may embody any machine that is configured to remove material fromthe mine and load the material onto one or more transport machines 120b. Non-limiting examples of excavators 120 a include, for example,bucket-type excavating machines, electromagnetic-lift devices, backhoeloaders, dozers, etc. Transport machines 120 b may embody any machinethat is configured to transport materials within work environment 100such as, for example, articulated trucks, dump trucks, or any othertruck adapted to transport materials. The number, sizes, and types ofmachines illustrated in FIG. 1 are exemplary only and not intended to belimiting. Accordingly, it is contemplated that work environment 100 mayinclude additional, fewer, and/or different components than those listedabove. For example, work environment 100 may include a skid-steerloader, a track-type tractor, material transfer vehicle, or any othersuitable fixed or mobile machine that may contribute to the operation ofwork environment 100.

In one embodiment, each of machines 120 a, 120 b may include on-boarddata collection and communication equipment to monitor, collect, and/ordistribute information associated with one or more components ofmachines 120 a, 120 b. As shown in FIG. 2, machines 120 a, 120 b mayeach include, among other things, one or more monitoring devices 121,such as sensors, electronic control modules, etc. (not shown) coupled toone or more data collectors 125 via communication lines 122; one or moretransceiver devices 126; and/or any other components for monitoring,collecting, and communicating information associated with the operationof machines 120 a, 120 b. Each of machines 120 a, 120 b may also beconfigured to receive information, warning signals, operatorinstructions, or other messages or commands from off-board systems, suchas a haul route management system 135. The components described aboveare exemplary and not intended to be limiting. Accordingly, thedisclosed embodiments contemplate each of machines 120 a, 120 bincluding additional and/or different components than those listedabove.

Monitoring devices 121 may include any device for collecting performancedata associated with one or more machines 120 a, 120 b. For example,monitoring devices 121 may include one or more sensors for measuring anoperational parameter such as engine and/or machine speed and/orlocation; fluid pressure, flow rate, temperature, contamination level,and or viscosity of a fluid; electric current and/or voltage levels;fluid (i.e., fuel, oil, etc.) consumption rates; loading levels (i.e.,payload value, percent of maximum payload limit, payload history,payload distribution, etc.); transmission output ratio, slip, etc.;grade; traction data; drive axle torque; intervals between scheduled orperformed maintenance and/or repair operations; and any otheroperational parameter of machines 120 a, 120 b. In one embodiment,transport machines 120 b may each include at least one torque sensor 121a for monitoring a torque applied to the drive axle. Alternatively,torque sensor 121 a may be configured to monitor a parameter from whichtorque on the drive axle may be calculated or derived. It iscontemplated that one or more monitoring devices 121 may be configuredto monitor certain environmental features associated with workenvironment 100. For example, one or more machines 120 a, 120 b mayinclude an inclinometer for measuring an actual grade associated with asurface upon which the machine is traveling.

Data collector 125 may be configured to receive, collect, package,and/or distribute performance data collected by monitoring devices 121.Performance data, as the term is used herein, refers to any type of dataindicative of at least one operational aspect associated with one ormore machines 120 or any of its constituent components or subsystems.Non-limiting examples of performance data may include, for example,health information such as fuel level, oil pressure, engine temperature,coolant flow rate, coolant temperature, tire pressure, or any other dataindicative of the health of one or more components or subsystems ofmachines 120 a, 120 b. Alternatively and/or additionally, performancedata may include status information such as engine power status (e.g.,engine running, idle, off), engine hours, engine speed, machine speed,machine location and speed, current gear that the machine is operatingin, or any other data indicative of a status of machine 120. Optionally,performance data may also include certain productivity information suchas task progress information, load vs. capacity ratio, shift duration,haul statistics (weight, payload, etc.), fuel efficiency, or any otherdata indicative of a productivity of machine 120. Alternatively and/oradditionally, performance data may include control signals forcontrolling one or more aspects or components of machines 120 a, 120 b.Data collector 125 may receive performance data from one or moremonitoring devices via communication lines 122 during operations of themachine. According to one embodiment, data collector 125 mayautomatically transmit the received data to haul route management system135 via communication network 130. Alternatively or additionally, datacollector 125 may store the received data in memory for a predeterminedtime period, for later transmission to haul route management system 135.For example, if a communication channel between the machine and haulroute management system 135 becomes temporarily unavailable, theperformance data may be retrieved for subsequent transmission when thecommunication channel has been restored.

Communication network 130 may include any network that provides two-waycommunication between machines 120 a, 120 b and an off-board system,such as haul route management system 135. For example, communicationnetwork 130 may communicatively couple machines 120 a, 120 b to haulroute management system 135 across a wireless networking platform suchas, for example, a satellite communication system. Alternatively and/oradditionally, communication network 130 may include one or morebroadband communication platforms appropriate for communicativelycoupling one or more machines 120 a, 120 b to haul route managementsystem 135 such as, for example, cellular, Bluetooth, microwave,point-to-point wireless, point-to-multipoint wireless,multipoint-to-multipoint wireless, or any other appropriatecommunication platform for networking a number of components. Althoughcommunication network 130 is illustrated as a satellite wirelesscommunication network, it is contemplated that communication network 130may include wireline networks such as, for example, Ethernet, fiberoptic, waveguide, or any other type of wired communication network.

Haul route management system 135 may include one or more hardwarecomponents and/or software applications that cooperate to improveperformance of a haul route by monitoring, analyzing, optimizing, and/orcontrolling performance or operation of one or more individual machines.Haul route management system 135 may include a condition monitoringsystem 140 for collecting, distributing, analyzing, and/or otherwisemanaging performance data collected from machines 120 a, 120 b. Haulroute management system 135 may also include a torque estimator 150 fordetermining a drive axle torque, estimating a total effective grade,calculating a rolling resistance, and/or determining other appropriatecharacteristics that may be indicative of the performance of a machineor machine drive train. Haul route management system 135 may alsoinclude a performance simulator 160 for simulating performance models ofslow machines and optimizing payload of slow machines in order tomaintain a desired speed of the haul route.

Condition monitoring system 140 may include any computing systemconfigured to receive, analyze, transmit, and/or distribute performancedata associated with machines 120 a, 120 b. Condition monitoring system140 may be communicatively coupled to one or more machines 120 viacommunication network 130. Condition monitoring system 140 may embody acentralized server and/or database adapted to collect anddisseminate-performance data associated with each of machines 120 a, 120b. Once collected, condition monitoring system 140 may categorize and/orfilter the performance data according to data type, priority, etc. Inthe case of critical or high-priority data, condition monitoring system140 may be configured to transmit “emergency” or “critical” messages toone or more work site personnel (e.g., repair technician, projectmanagers, etc.) indicating that a remote asset has experienced acritical event. For example, should a machine become disabled, enter anunauthorized work area, or experience a critical engine operationcondition, condition monitoring system 140 may transmit a message (textmessage, email, page, etc.) to a project manager, job-site foreman,shift manager, machine operator, and/or repair technician, indicating apotential problem with the machine.

Condition monitoring system 140 may include hardware and/or softwarecomponents that perform processes consistent with certain disclosedembodiments. For example, as illustrated in FIG. 2, condition monitoringsystem 140 may include one or more transceiver devices 126, a centralprocessing unit (CPU) 141, a communication interface 142, one or morecomputer-readable memory devices, including storage device 143, a randomaccess memory (RAM) module 144, and a read-only memory (ROM) module 145,a display unit 147, and/or an input device 148. The components describedabove are exemplary and not intended to be limiting. Furthermore, it iscontemplated that condition monitoring system 140 may includealternative and/or additional components than those listed above.

CPU 141 may be one or more processors that execute instructions andprocess data to perform one or more processes consistent with certaindisclosed embodiments. For instance, CPU 141 may execute software thatenables condition monitoring system 140 to request and/or receiveperformance data from data collector 125 of machines 120 a, 120 b. CPU141 may also execute software that stores collected performance data instorage device 143. In addition, CPU 141 may execute software thatenables condition monitoring system 140 to analyze performance datacollected from one or more machines 120 a, 120 b, modify one or moreproduction aspects of the machine (e.g., production schedule, productrelease date, production budget, etc.), improve a component parameterbased on one or more predefined specifications associated with thecomponent, and/or provide customized operation analysis reports,including recommendations for component optimization and/or design.

CPU 141 may be connected to a common information bus 146 that may beconfigured to provide a communication medium between one or morecomponents associated with condition monitoring system 140. For example,common information bus 146 may include one or more components forcommunicating information to a plurality of devices. CPU 141 may executesequences of computer program instructions stored in computer-readablemedium devices such as, for example, a storage device 143, RAM 144,and/or ROM 145 to perform methods consistent with certain disclosedembodiments, as will be described below.

Communication interface 142 may include one or more elements configuredfor two-way data communication between condition monitoring system 140and remote systems (e.g., machines 120 a, 120 b) via transceiver device126. For example, communication interface 142 may include one or moremodulators, demodulators, multiplexers, demultiplexers, networkcommunication devices, wireless devices, antennas, modems, or any otherdevices configured to support a two-way communication interface betweencondition monitoring system 140 and remote systems or components.

One or more computer-readable medium devices may include storage devices143, a RAM 144, ROM 145, and/or any other magnetic, electronic, flash,or optical data computer-readable medium devices configured to storeinformation, instructions, and/or program code used by CPU 141 ofcondition monitoring system 140. Storage devices 143 may includemagnetic hard-drives, optical disc drives, floppy drives, flash drives,or any other such information storing device. A random access memory(RAM) device 144 may include any dynamic storage device for storinginformation and instructions by CPU 141. RAM 144 also may be used forstoring temporary variables or other intermediate information duringexecution of instructions to be executed by CPU 141. During operation,some or all portions of an operating system (not shown) may be loadedinto RAM 144. In addition, a read only memory (ROM) device 145 mayinclude any static storage device for storing information andinstructions by CPU 141.

Condition monitoring system 140 may be configured to analyze performancedata associated with each of machines 120 a, 120 b. According to oneembodiment, condition monitoring system 140 may include diagnosticsoftware for analyzing performance data associated with one or moremachines 120 a, 120 b based on threshold levels (which may be factoryset, manufacturer recommended, and/or user configured) associated with arespective machine. For example, diagnostic software associated withcondition monitoring system 140 may compare an engine temperaturemeasurement received from a particular machine with a predeterminedthreshold engine temperature. If the measured engine temperature exceedsthe threshold temperature, condition monitoring system 140 may generatean alarm and notify one or more of the machine operator, job-sitemanager, repair technician, dispatcher, or any other appropriate entity.

In accordance with another embodiment, condition monitoring system 140may be configured to monitor and analyze productivity associated withone or more of machines 120 a, 120 b. For example, condition monitoringsystem 140 may include productivity software for analyzing performancedata associated with one or more machines 120 a, 120 b based onuser-defined productivity thresholds associated with a respectivemachine. Productivity software may be configured to monitor theproductivity level associated with each of machines 120 a, 120 b andgenerate a productivity report for a project manager, a machineoperator, a repair technician, or any other entity that may subscribe tooperator or machine productivity data (e.g., a human resourcesdepartment, an operator training and certification division, etc.)According to one exemplary embodiment, productivity software may comparea productivity level associated with a machine (e.g., amount of materialmoved by a particular machine) with a predetermined productivity quotaestablished for the respective machine. If the productivity level isless than the predetermined quota, a productivity notification may begenerated and provided to the machine operator and/or project manager,indicating the productivity drop of the machine.

Condition monitoring system 140 may be in data communication with one ormore other back-end systems and may be configured to distribute certainperformance data to these systems for further analysis. For example,condition monitoring system 140 may be communicatively coupled to atorque estimator 150 and may be configured to provide performance dataassociated with the machine drive axle to torque estimator 150.Alternatively or additionally, condition monitoring system 140 may be indata communication with a performance simulator 160 and may beconfigured to provide performance data to performance simulator 160 forfurther analysis. Although torque estimator 150 and performancesimulator 160 are illustrated as standalone systems that are external tocondition monitoring system 140, it is contemplated that one or both oftorque estimator 150 and performance simulator 160 may be included as asubsystem of condition monitoring system 140.

Torque estimator 150 may include a hardware or software moduleconfigured to receive/collect certain performance data from conditionmonitoring system 140 and determine, based on the received operationdata, a drive axle torque associated with one or more machines 120 a,120 b. Torque estimator 150 may be configured to determine a drive axletorque based on performance data collected by torque sensor 121 a.Alternatively or additionally, drive axle torque may be estimated basedon the performance data and the known design parameters of the machine.For example, based on an engine operating speed and the operating gear,torque estimator 150 may access an electronic look-up table and estimatethe drive axle torque of the machine at a particular payload weightusing the look-up table.

Once an estimated machine drive axle torque is determined, torqueestimator 150 may estimate a total effective grade for the one or moremachines. For example, torque estimator 150 may estimate a totaleffective grade (TEG) value as:

${TEG} = {\frac{RP}{GMW} - \frac{MA}{AG}}$where RP refers to machine rimpull, GMW refers to gross machine weight,MA refers to the acceleration of the machine, and AG refers to theactual grade of the terrain on which that machine is located. Grossmachine weight and machine acceleration may be monitored using on-boarddata monitoring devices 121. Actual grade may be estimated based onmonitored performance data. For example, actual grade may be determinedusing precision GPS data gathered from on-board GPS equipment. Forexample, performance data may include provide latitude, longitude, andelevation of the machine. The actual grade may be determined bycalculating ratio between the vertical change in position (based on theelevation data associated with the GPS data) and the horizontal changein position (based on the latitude and longitude data associated withthe GPS data). Alternatively or additionally, actual grade may becalculated using an on-board data monitoring device such as, forexample, an inclinometer. Rim pull may be determined as:

${RP} = \frac{{DAT} \times {LPTR} \times {PTE}}{TDRR}$where DAT refers to the torque applied to the machine drive axle, LPTRrefers to the lower power train reduction factor, PTE refers to theefficiency of the power train, and TDRR refers to the dynamic rollingradius of the tire. Lower power train reduction may be determined bymonitoring a change in gear during real-time calculation of rim pull.Power train efficiency may be calculated based on real-time performancedata collected from the machine. Tire dynamic rolling radius may beestimated based on a monitored tire pressure, speed, and gross machineweight.

Once total effective grade has been determined, torque estimator 150 maydetermine a rolling resistance associated with one or more of machines120 a, 120 b. A rolling resistance value may be calculated as:RR=TEG−(AG+EL)where EL refers to the efficiency loss of the machine. Efficiency lossmay be estimated as the difference between input power efficiency andoutput power efficiency, which may be estimated based on empirical testdata at particular engine operating speeds and loading conditions. Asexplained, actual grade may be determined based on calculationsassociated with collected GPA data and/or monitored using an on-boardinclinometer.

Performance simulator 160 may be configured to simulate performance ofmachines 120 a, 120 b under various operational or environmentalconditions. Based on the simulation results, performance simulator 160may determine ideal or optimal operating conditions to achieve a desiredperformance of machines 120 a, 120 b and/or work environment 100.According to one embodiment, performance simulator 160 may be any typeof computing system that includes component or machine simulatingsoftware. The simulating software may be configured to build ananalytical model corresponding to a machine or any of its constituentcomponents based on empirical data collected from real-time operationsof the machine. Once the model is built, performance simulator 160 mayanalyze the model under specific operating conditions (e.g., loadconditions, environmental conditions, terrain conditions, etc.) andgenerate simulated performance data of the machine based on thespecified conditions.

According to one embodiment, performance simulator 160 may include idealdesign models associated with each of machines 120 a, 120 b. These idealmodels can be electronically simulated to generate ideal performancedata (i.e., data based on ideal performance of machine and itsconstituent components). Those skilled in the art will recognize that,as a machine ages, components associated with the machine may begin toexhibit non-ideal behavior, due to normal wear, stress, and/or damage tothe machine during operation. In order to provide more realisticperformance simulations consistent with these non-idealities, the idealmodels may be edited based on actual performance data collected frommachines 120 a, 120 b, thereby creating actual or empirical models of arespective machine and/or its individual components.

Performance simulator 160 may simulate the actual models to predictperformance and productivity of the machine under a variety of operatingconditions. For example, performance simulator 160 may simulate anactual model of hauler 120 b under a multiple payload and/or haul routeconditions to determine a speed, torque output, engine condition, fuelconsumption rate, haul route completion time, etc. associated with eachsimulated condition. In one embodiment, performance simulator 160 may beconfigured to select a payload level for the machine to meet a desiredoperating condition of the haul route or work environment 100. Forexample, a user of performance simulator 160 may specify a desired speedof the machine required to maintain a productivity requirement of thehaul route. Performance simulator 160 may simulate operation of themachine at multiple payload levels and estimate the desired speed of themachine at each payload level. Performance simulator 160 may select thepayload level that allows the machine to meet the user-specified speedrequirements.

In another example, a user of performance simulator 160 may specify afuel consumption limit for the machine. Performance simulator 160 maysimulate an operation of the machine under multiple load conditions andidentify one or more payload levels that, if selected, allow the machineto operate within the fuel consumption limitations specified by theuser.

Performance simulator 160 may also include a diagnostic and/orprognostic simulation tool that simulates actual machine models (i.e.,models derived or created from actual machine data) to predict acomponent failure and/or estimate the remaining lifespan of a particularcomponent or subsystem of the machine. For example, based on performancedata associated with the engine and/or transmission, performancesimulator 160 may predict the remaining lifespan of the engine, drivetrain, differential, or other components or subsystems of the machine.Accordingly, performance simulator 160 may predict how changes in apayload profile for a machine may affect the lifespan of one or more ofthese components. For instance, performance simulator 160 may estimatethat, if payload for a particular hauler 120 b is reduced by 10%, theremaining lifespan of the drive train may increase by 15%. Performancesimulator 160 may periodically report this data to a mine operator,project manager, machine operator, and/or maintenance department of workenvironment 100.

It is contemplated that one or more of condition monitoring system 140,torque estimator 150, and/or performance simulator 160 may be includedas a single, integrated software package or hardware system.Alternatively or additionally, these systems may embody separatestandalone modules configured to interact or cooperate to facilitateoperation of one or more of the other systems. For example, while torqueestimator 150 is illustrated and described as a standalone system,separate from performance simulator 160, it is contemplated that torqueestimator 150 may be included as a software module configured to operateon the same computer system as performance simulator 160.

Performance simulator 160 may be configured to generate payloadrequirements 165 for one or more vehicles operating in work environment100. According to one embodiment, payload requirements 165 may includeloading limits for one or more machines 120 a, 120 b that increase orenhance performance of the one or more machines 120 a, 120 b and/or workenvironment 100. For example, performance simulator 160 may identify anunderperforming or slow machine and determine, based on the performancedata associated with the machine, an optimal payload limit for themachine that enables the machine to maintain a desired speed.Performance simulator 160 may generate payload requirements 165 for themachine that specify the payload limits of the machine required tomaintain a desired machine speed.

Payload requirements 165 may include paper-based or electronic reportsthat list machines whose payload levels are modified or prescribed to belower than a maximum payload level for the machine. Thus, payloadrequirements 165 may be associated with any machine that performancesimulator 160 prescribes to be loaded at less than a maximum loadinglevel associated with the machine. According to one embodiment, payloadrequirements 165 may be delivered electronically (using email, textmessage, facsimile, etc.) or via any other appropriate format.

Performance simulator 160 may provide payload requirements 165 to one ormore designated subscribers 170 of payload requirement data. Subscribers170 may include, for example, operators of one or more transportmachines 120 b listed in the payload requirements 165, operators of oneor more machines (e.g., automatic loading machines (conveyor belts,buckets, etc.), excavators 120 a, etc.) responsible for loadingtransport machines 120 b, project managers, mine owners, repairtechnicians, shift managers, human resource personnel, or any otherperson or entity that may be designated to receive payload requirements165.

Processes and methods consistent with the disclosed embodiments mayenable optimization of a haul route based on real-time performance ofone or more machines 120 a, 120 b operating in work environment 100 byproviding a system that combines real-time data monitoring andcollection capabilities with performance analysis and simulation tools.Specifically, the features and methods described herein allow projectmanagers, equipment owners, and/or mine operators to effectivelyidentify slow or underperforming machines, analyze performance dataassociated with these machines to establish or adjust payload limitsthat may enhance the speed and/or performance of the machines.Optionally, features and methods described herein may be configured todiagnose and/or correct any potential causes of deficient performance.FIGS. 3 and 4 provide flowcharts 300 and 400, respectively, whichillustrate exemplary payload optimization methods associated with haulroute management system 135.

As illustrated in FIG. 3, the payload optimization process may commencewith the collection of machine performance data (Step 301). For example,condition monitoring system 140 of haul route management system 135 mayreceive/collect performance data from each machine operating in workenvironment 100. According to one embodiment, condition monitoringsystem 140 may automatically receive this data from data collectors 125associated with each of machines 120 a, 120 b. Alternatively oradditionally, condition monitoring system 140 may provide a data requestto each of machines 120 a, 120 b and receive performance data from eachmachine in response to the request.

Once performance data has been received, a drive axle torque of themachine may be determined based on the performance data (Step 302). Forexample, torque estimator 150 may determine the drive axle torque basedon data received from torque sensor 121 a. Alternatively, torqueestimator 150 may determine drive axle torque using electronic look-uptables (compiled from empirical test data associated with the type andmodel of machine) based on engine operating conditions, gear selection,and other data received from the machine.

As explained, once drive axle torque has been determined/estimated,torque estimator 150 may calculate/estimate a total effective gradeassociated with each machine (Step 303) and determine if thecalculated/estimated total effective grade is greater than a thresholdvalue (Step 304). The threshold value for total effective grade may bepredetermined, user defined, or calculated based on operations ofsimilar types of machines in work environment 100. For example, thethreshold value for hauler 120 b may be an average total effective gradeassociated with all similar makes and models of machines operatingduring haul route operations.

If the calculated total effective grade of the machine is less than orequal to the threshold value (Step 304: No) (which may indicate that themachine is operating normally), the process may proceed to Step 301 andcontinue data collection and monitoring activities for machineenvironment 100. If, on the other hand, the calculated total effectivegrade is greater than the threshold value (Step 304: Yes) (which mayindicate that the machine is a slow or under-performing machine), theideal (i.e., design) performance of the machine may be estimated usingthe calculated total effective grade (Step 305). Design performance maybe estimated by simulating an ideal model of the machine. According toan exemplary embodiment, performance simulator 160 may simulateoperation of the ideal model of the machine at the calculated totaleffective grade for the machine to obtain ideal performance dataassociated with the monitored total effective grade of the machine.

The design performance data collected from the simulated ideal machinemodel may be compared to the collected performance data (Step 306). Forexample, performance simulator 160 may compare ideal engine operatingparameters generated by the simulated operation of machine at the actualtotal effective grade value with actual operating parameters collectedfrom the machine. For a normal, healthy machine the actual engineoperating parameters should be relatively consistent with the idealengine operating parameters (less minor efficiency and performancelosses associated with normal wear and tear). Thus, actual engineoperating parameters that are inconsistent with the ideal engineoperating parameters may be an indication that the machine is slow orunderperforming.

In determining whether the actual engine operating parameters areconsistent with the ideal engine operating parameters, performancesimulator 160 may determine whether the actual parameters are within anacceptable range of the ideal parameters. These acceptable ranges may bepredetermined and/or user-defined. Furthermore, the size of acceptableranges may be dependent upon the parameter under investigation. Forexample, in work environments where operating speed of each machine iscritical to the productivity of work environment 100 (e.g., work siteshaving only one haul route where bottlenecks may substantially affecthaul route productivity), the acceptable range for speed may be morestrict than an acceptable range for another parameter (e.g., fuelconsumption). In contrast, for work environments where fuel consumptionis important, the acceptable range for fuel economy may be more strictthan that for speed.

If the actual performance data for the machine is consistent with theideal performance data (Step 306: Yes), indicating that the machine isoperating normally, performance simulator 160 may determine that themachine requires no operational adjustment or regulation. Accordingly,the process may continue to Step 301 and continue monitoring machineperformance data associated with work environment 100.

In contrast, if the actual performance data for the machine is notconsistent with the ideal performance data (Step 306: No), performancesimulator 160 may establish/adjust a payload limit for the machine (Step307). For example, if the actual performance data indicates that aparticular machine is slower and/or less efficient than other machinesoperating in work environment 100, performance simulator 160 maydetermine a payload limit that eases the strain on the slower machinethat enables the machine to keep pace with faster machines.

Performance simulator 160 may estimate the optimum payload limit bysimulating performance of the machine under multiple payload conditions.For example, performance simulator 160 may select a first payload levelfor the machine, whereby the first payload level is less than thepayload level present during the machine's underperforming operations(Step 308). Performance simulator 160 may simulate the machine speed atthe first payload level (Step 309) and determine whether the speed iswithin a desired speed threshold (Step 310). If the simulated speed iswithin the desired speed threshold (Step 310: Yes), performancesimulator 160 may establish the payload limit as the first payloadlevel. If, on the other hand, the simulated speed is not within thedesired speed threshold (Step 310: No), performance simulator 160 mayselect a second payload level (Step 311). Performance simulator 160 mayrepeat Steps 309 and 310 until a suitable payload level (i.e., one thatcauses the machine to conform to the desired speed threshold) has beenidentified.

Upon identification of a suitable payload level that conforms to thedesired speed requirement of work environment 100, performance simulator160 may generate a payload notification (Step 313). Payload notificationmay include an electronic message, which may be transmitted to a workenvironment manager, a haul machine operator, an excavator operator, amine operator, or a machine dispatcher. By generating and distributing apayload notification, indicating recommended payload limits for slow orunderperforming machines, haul route management system 135 may morequickly and efficiently resolve work environment performanceinefficiencies by leveraging real-time data collection capabilities withperformance simulation and modeling techniques.

It is contemplated that additional and/or different methods may be usedto identify a slow or underperforming machine. For example, as analternative or in addition to identifying a slow or underperformingmachine based on total effective grade and machine speed datacalculations, underperforming machines may be identified based bymonitoring route completion time for one or more machines. FIG. 4provides a flowchart 400 illustrating an exemplary method for managing ahaul route based on route completion time.

As illustrated in FIG. 4, condition monitoring system 140 mayreceive/collect machine performance data associated with each machineoperating in work environment 100 (Step 410). Based on the receivedperformance data, condition monitoring system 140 may determine/estimatea route completion time associated with each machine (Step 420). Forexample, a haul route may be stored in memory associated with conditionmonitoring system 140. During operations of the machine, conditionmonitoring system 140 may monitor location of each machine usingposition sensors, GPS, or any other suitable device or system formonitoring a machine position. To determine a haul route completiontime, condition monitoring system 140 may record times when each machinereaches designated starting and stopping points of the route. Conditionmonitoring system 140 may subsequently calculate an actual haul routecompletion time as the elapsed operating time between the route startingand stopping points.

Condition monitoring system 430 may compare the actual route completiontime with a threshold completion time (Step 430) and determine whetherthe actual route completion time exceeds the threshold completion time(Step 440). According to one embodiment, threshold completion time maybe determined based on machine test data by conducting several test haulroute runs with a normal, healthy machine.

If the actual route completion time for a particular machine is lessthan or equal to the threshold completion time (Step 440: No)(indicating that the performance of the machine is normal), the processmay proceed to Step 410 and continue monitoring the performance ofmachines 120 a, 120 b. If, however, the actual route completion timeexceeds a threshold completion time (Step 440: Yes), performancesimulator 160 may select a first payload level (Step 450) and simulatemachine operation to estimate a simulated completion time for themachine based on the selected payload level (Step 460).

The simulated completion time may be compared with the thresholdcompletion time to predict whether the selected payload level issufficient to enable the machine to complete the haul route run withinthe threshold completion time (Step 470). If the simulated completiontime is less than or equal to the threshold completion time, performancesimulator 160 may establish a payload limit as the selected payloadlevel for the machine (Step 480). Accordingly, a payload limit and/orthe simulated test results may be provided to a project manager, a mineoperator, a machine operation, a machine dispatcher, or any person orentity authorized to prescribe payload adjustments for one or moremachines.

If, however, the simulated completion time exceeds the thresholdcompletion time (Step 470: No), performance simulator 160 may select analternative payload level (less than the first selected payload level)(Step 490). The process may then proceed to Step 460 to repeat the haulroute simulation. The processes associated with modifying the payloadlimit and simulating the haul route run (Step 460, 470, and 490) may berepeated until a payload limit that enables the machine to conform tothe threshold completion time has been identified.

It is contemplated that the methods associated with FIGS. 3 and 4 mayalso be used as diagnostic tools to determine whether a particular slowor underperforming machine requires maintenance. In one embodiment,performance simulator 160 may be configured to provide a performancealert if performance simulations reveal that, in order for a machine tomeet the speed or time requirements associated with a haul route, themachine must be operated at or below a predetermined or user-definedminimum percentage of a rated payload capacity. Accordingly, if theperformance of the machines degrades to such a point so as to cause thepayload limit to fall below the minimum acceptable value, performancesimulator 160 may provide a diagnostic warning to a work environmentmanagement, a mine operator, an operator of the machine, or a repairtechnician.

INDUSTRIAL APPLICABILITY

Methods and systems consistent with the disclosed embodiments mayprovide a haul route management solution that integrates real-timeequipment monitoring systems and performance-based analysis andsimulation tools to identify a target payload level for each machineimproves performance and/or productivity of work environment 100. Workenvironments that employ processes and features described herein providean automated system for detecting slow or unproductive machines and,using performance data collected from each machine during real-timeoperations of the machines, estimating an optimum payload level toachieve the desired performance level.

Although the disclosed embodiments are described in connection with workenvironments involving haul routes for mining equipment, they may beapplicable to any work environment where it may be advantageous toidentify underperforming machines that have a negative impact on theproductivity of other machines or a fleet of machines. According to oneembodiment, the presently disclosed haul route management system andassociated methods may be implemented as part of a connected worksiteenvironment that monitors performance data associated with a machinefleet and diagnoses potential problems with machines in the fleet. Assuch, the haul route management system may enable both health andproductivity monitoring of a work environment using real-timeperformance data associated with the one or more machines.

The presently disclosed haul route management system and associatedmethods may have several advantages. For example, because the haul routemanagement system is configured to identify slow or underperformingmachines and determine ideal payload limits for these machines, mineoperators or work environment managers may be able to efficiently modifyoperations of individual machines to conform to desired operations ofthe haul route, without jeopardizing productivity of other machines. Incontrast with conventional systems that may reduce or limit the speed offaster machines to yield to slower machines, the presently disclosedsystem provides a solution to identify slow machines and takeappropriate action to ensure that the slower machines keep pace withfaster machines.

Furthermore, the presently disclosed haul management system 135 may havecertain cost advantages over conventional systems. For example, becausethe haul management systems and associated methods described herein areconfigured to identify target payload levels of one or more machinesbased on desired performance characteristics of the machine and/or thehaul route, mine operators or project managers may adjust the payloadrequirements of the fleet so as to minimize fuel consumption or prolongcomponent lifespan. As a result, fuel costs and costs associated withpremature component failure (e.g., repair costs, productivity costsassociated with machine downtime, etc.) may be limited or reduced.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the disclosedperformance-based haulage management system and associated methodswithout departing from the scope of the invention. Other embodiments ofthe present disclosure will be apparent to those skilled in the art fromconsideration of the specification and practice of the presentdisclosure. It is intended that the specification and examples beconsidered as exemplary only, with a true scope of the presentdisclosure being indicated by the following claims and theirequivalents.

1. A computer-implemented method for managing haul routes in workenvironments, comprising: collecting, at a processor associated with acomputer, performance data associated with a machine during a haul routerun in a work environment; monitoring, by the processor, a routecompletion time associated with the machine; comparing, by theprocessor, the route completion time with a threshold completion time;selecting, by the processor, a first payload level for the machine ifthe route completion time exceeds the threshold completion time;simulating, by the processor, operation of the machine based on thefirst payload level to generate a simulated route completion time of themachine; and establishing, by the processor, a payload limit of themachine as the first payload level if the simulated route completiontime does not exceed the threshold completion time.
 2. Thecomputer-implemented method of claim 1, further including: selecting, ifthe simulated route completion time exceeds the threshold completiontime, a second payload level for the machine; simulating operation ofthe machine based on the second payload level to generate a secondsimulated route completion time of the machine; and establishing thepayload limit for the machine as the second payload level if the secondsimulated route completion time does not exceed the threshold completiontime.
 3. The computer-implemented method of claim 1, further including:generating a payload notification indicative of the payload limit forthe machine; and providing the payload notification to a payloadsubscriber.
 4. The computer-implemented method of claim 1, furtherincluding: analyzing, if the route completion time exceeds the thresholdcompletion time, the collected performance data to diagnose a potentialcause of inconsistency between the route completion time and thethreshold completion time; and generating a report that summarizes thepotential cause of the inconsistency and provides recommendations forresolving the potential cause of the inconsistency.
 5. Thecomputer-implemented method of claim 4, wherein analyzing the collecteddata includes: estimating an actual remaining lifespan associated withone or more components of the machine based on a current payload level;and generating a simulated remaining lifespan associated with one ormore of the components of the machine based on the established payloadlimit; wherein the report further summarizes a difference between theestimated remaining lifespan and the simulated remaining lifespan. 6.The computer-implemented method of claim 1, wherein simulating operationof the machine includes: generating a model of the machine based on thecollected performance data; and simulating operation of the machineusing the generated model.
 7. A computer-implemented method for managinghaul routes in work environments, comprising: collecting, at a processorassociated with a computer, performance data associated with a machineoperating in a work environment; determining, by the processor, a driveaxle torque of the machine and calculating a total effective gradeassociated with the machine based on the drive axle torque; comparing,by the processor, calculated total effective grade with a thresholdlevel; simulating, by the processor, a design performance of the machinebased on the calculated total effective grade if the calculated totaleffective grade exceeds the threshold level; comparing, by theprocessor, design performance data with the collected performance data;and adjusting, by the processor, a payload limit of the machine if thedesign performance is inconsistent with collected performance data. 8.The computer-implemented method of claim 7, wherein determining thedrive axle torque includes monitoring the drive axle torque with atorque sensor.
 9. The computer-implemented method of claim 7, whereindetermining the drive axle torque includes estimating the drive axletorque based on the collected performance data.
 10. Thecomputer-implemented method of claim 7, wherein the threshold levelcorresponds to a total effective grade associated with one or more othermachines operating in the work environment.
 11. The computer-implementedmethod of claim 7, wherein the performance data includes speed of themachine.
 12. The computer-implemented method of claim 7, whereinadjusting the payload limit of the machine includes: selecting a firstpayload level for the machine; simulating a speed of the machine basedon the first payload level; and determining whether the simulated speedis within a target speed threshold; establishing, if the simulated speedis within a target speed threshold, the payload limit for the machine asthe first payload level; and selecting, if the simulated speed is notwithin a target speed threshold, a second payload level for the machineand simulating the speed of the machine based on the second payloadlevel.
 13. The computer-implemented method of claim 12, furtherincluding: generating a payload notification indicative of the payloadlimit for the machine; and providing the payload notification to apayload subscriber.
 14. The computer-implemented method of claim 13,wherein the payload notification includes a command signal for limitingan amount of material loaded into the machine by an automated loadingdevice.
 15. The computer-implemented method of claim 7, furtherincluding: analyzing, if the estimated total effective grade exceeds thethreshold level, the collected performance data to determine a potentialcause of inconsistency between the collected performance data and thedesign performance data; and generating a report that summarizes thepotential cause of the inconsistency and provides recommendations forresolving the potential cause of the inconsistency.
 16. Thecomputer-implemented method of claim 15, wherein analyzing the collecteddata includes: estimating an actual remaining lifespan associated withone or more components of the machine based on a current payload level;and generating a simulated remaining lifespan associated with one ormore of the components of the machine based on the established payloadlimit; wherein the report further summarizes a difference between theestimated remaining lifespan and the simulated remaining lifespan.